Muhamad Agung Gumelar, - (2023) SISTEM DETEKSI PENYAKIT PARU-PARU MELALUI CITRA X-RAY THORAX DENGAN ALGORITMA CONVOLUTIONAL NEURAL NETWORK (CNN) DAN DEEP HYBRID LEARNING (DHL) BERBASIS APLIKASI WEBSITE. S1 thesis, Universitas Pendidikan Indonesia.
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Abstract
Pendeteksian penyakit paru-paru melalui citra X-Ray Thorax memerlukan tenaga medis dan biasanya dilakukan secara manual, sehingga pemanfaatan dari salah satu Artifial Intelegence (AI) yaitu Deep Learning (DL) dapat menjadi solusi untuk mendeteksi jenis penyakit paru-paru secara otomatis dan relatif lebih cepat. Penelitian ini bertujuan untuk menghasilkan sebuah sistem deteksi penyakit paru-paru melalui citra X-Ray Thorax menggunakan algoritma Convolutional Neural Network (CNN) dan Deep Hybrid Learning (DHL) berbasis aplikasi website. Metode yang digunakan dalam penelitian yaitu metode experimen dengan menggunakan bahasa pemrograman python untuk membuat model DHL dan aplikasi website. Hasil penelitian menunjukan kinerja model DHL yang paling baik terdapat pada model hybrid 4 dengan nilai accuracy 96,75%, presicion 97,01%, dan recall 96,75%. Pendeteksian menggunakan aplikasi website yang telah terintegrasi dengan model hybrid 4 dilakukan menggunakan 40 sampel data uji citra X-Ray Thorax. Dari hasil pendektesian tersebut, diperoleh 36 sampel terprediksi tepat dan 4 sampel lainnya salah prediksi. Dengan mengunggah file citra X-Ray Thorax di halaman website, maka data tersebut akan tersimpan di dalam database dan secara otomatis jenis penyakit paru-paru dapat terdeteksi. Secara keseluruhan, penelitian ini berhasil menghasilkan sistem deteksi penyakit paru-paru yang memberikan hasil deteksi yang cukup akurat. Detection of lung disease using Thorax X-Ray images requires medical personnel and used to be done manually, therefore the use of Artificial Intelligence (AI), namely Deep Learning (DL) can be a solution detection lung disease automatically and quickly. This study aims to yield lung disease detection system using X-Ray images of the Thorax with Convolutional Neural Network (CNN) and Deep Hybrid Learning (DHL) algorithms based on web applications. The method used in this research is an experimental method using the python programming language to create DHL model and website application. The results showes that the best performance of the DHL model is found in the hybrid 4 model with an accuracy, precision and recall are 96.75%, 97.01%, and 96.75%, respectively. Detection using a website application that has been integrated with a hybrid model was performed using 40 samples of Thorax X-Ray image test data. From the results of the detection, 36 samples were predicted correctly and 4 other samples were mispredicted. By uploading the X-Ray Thorax image file on the website page, the data will be stored in the database and automatically the type of lung disease can be detected. Overall, this research successfully produced a lung disease detection system that provides quite accurate detection results.
| Item Type: | Thesis (S1) |
|---|---|
| Additional Information: | ID SINTA Dosen Pembimbing: Erik Haritman: 6003383 Budi Mulyanti: 5993841 |
| Uncontrolled Keywords: | Citra X-Ray Thorax, Deep Hybrid Learning, penyakit paru-paru, Convolutional Neural Network Thorax X-Ray Image, Deep Hybrid Learning, Lung Disease, Convolutional Neural Network |
| Subjects: | L Education > L Education (General) T Technology > TK Electrical engineering. Electronics Nuclear engineering |
| Divisions: | Fakultas Pendidikan Teknologi dan Kejuruan > Jurusan Pendidikan Teknik Elektro |
| Depositing User: | Muhamad Agung Gumelar |
| Date Deposited: | 07 Sep 2023 05:01 |
| Last Modified: | 07 Sep 2023 05:01 |
| URI: | http://repository.upi.edu/id/eprint/103181 |
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